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He W.-P.,National Climate Center | Wang L.,Beijing Institute of Technology | Jiang Y.-D.,National Climate Center | Wan S.-Q.,Yangzhou Meteorological Office
Theoretical and Applied Climatology | Year: 2015

Parameter estimation is an important research topic in nonlinear dynamics. Based on the evolutionary algorithm (EA), Wang et al. (2014) present a new scheme for nonlinear parameter estimation and numerical tests indicate that the estimation precision is satisfactory. However, the convergence rate of the EA is relatively slow when multiple unknown parameters in a multidimensional dynamical system are estimated simultaneously. To solve this problem, an improved method for parameter estimation of nonlinear dynamical equations is provided in the present paper. The main idea of the improved scheme is to use all of the known time series for all of the components in some dynamical equations to estimate the parameters in single component one by one, instead of estimating all of the parameters in all of the components simultaneously. Thus, we can estimate all of the parameters stage by stage. The performance of the improved method was tested using a classic chaotic system—Rössler model. The numerical tests show that the amended parameter estimation scheme can greatly improve the searching efficiency and that there is a significant increase in the convergence rate of the EA, particularly for multiparameter estimation in multidimensional dynamical equations. Moreover, the results indicate that the accuracy of parameter estimation and the CPU time consumed by the presented method have no obvious dependence on the sample size. © 2015 Springer-Verlag Wien

He W.-P.,National Climate Center | Liu Q.-Q.,Nanjing University of Information Science and Technology | Jiang Y.-D.,National Climate Center | Lu Y.,Yangzhou Meteorological Office
Chinese Physics B | Year: 2015

In the present paper, a comparison of the performance between moving cutting data-rescaled range analysis (MC-R/S) and moving cutting data-rescaled variance analysis (MC-V/S) is made. The results clearly indicate that the operating efficiency of the MC-R/S algorithm is higher than that of the MC-V/S algorithm. In our numerical test, the computer time consumed by MC-V/S is approximately 25 times that by MC-R/S for an identical window size in artificial data. Except for the difference in operating efficiency, there are no significant differences in performance between MC-R/S and MC-V/S for the abrupt dynamic change detection. MC-R/S and MC-V/S both display some degree of anti-noise ability. However, it is important to consider the influences of strong noise on the detection results of MC-R/S and MC-V/S in practical application processes. © 2015 Chinese Physical Society and IOP Publishing Ltd.

He W.,National Climate Center | Wan S.,Yangzhou Meteorological Office | Jiang Y.,National Climate Center | Jin H.,Lanzhou University | And 3 more authors.
International Journal of Climatology | Year: 2013

An abrupt change occasionally occurs when the dynamical system suddenly shifts from one stable state to a new state, which can take place in many complex systems, such as climate, ecosystem, social system, and so on. In order to detect abrupt change, this article presents a novel method - sliding transformation parameter (STP) on the basis of skewness change and the Box-Cox transformation. Tests on model time series and 1000 simulated daily precipitation data show the ability of the present method to identify and detect abrupt change of probability density function. The applications of STP in daily precipitation data show that there is an abrupt climate change between 1979 and 1980 in the selected observational stations, which is almost the same with the result obtained by approximate entropy (ApEn). Furthermore, it is found that the sample sizes of sliding windows have some influence on the Lambda parameter of the Box-Cox transformation, but it does not significantly affect the varying trend of the parameter and the identification of the change point in annual or interannual time scale. Comparing STP with the coefficient of skewness and kurtosis, ApEn, and some statistics approaches (e.g. percentiles and annual maxima), we find that the performance of the present method is much better than that of these methods. © 2012 Royal Meteorological Society.

He W.,National Climate Center | Feng G.,National Climate Center | Wu Q.,National Satellite Meteorological Center | He T.,Jinan Environmental Monitoring Center | And 2 more authors.
International Journal of Climatology | Year: 2012

On the basis of detrended fluctuation analysis (DFA), a new method, moving cut data-DFA (MC-DFA), was presented to detect abrupt dynamic change in correlated time series. The numerical tests show the capability of the presented method to detect abrupt change time-instants in model time series generated by Logistic map. Moving DFA (MDFA) and approximate entropy (ApEn) can provide some information such as a single time-instant of abrupt dynamic change, but both of them cannot exactly detect all of the abrupt change regions. Some traditional methods, such as moving t-test, Cramer method, Mann-Kendall test and Yamamoto method, even cannot provide any information of abrupt dynamic change in these model time series. Meanwhile, results showed that windows sizes and strong noise have some less effect on the MC-DFA results. In summary, MC-DFA provides a reliable measure to detect the abrupt dynamic change in correlated time series, and perfectively make up the deficiencies of MDFA and ApEn. The applications in daily surface air pressure records further verify the validity of the present method. © 2011 Royal Meteorological Society.

He W.-P.,National Climate Center | Wang L.,Beijing Institute of Technology | Wan S.-Q.,Yangzhou Meteorological Office | Liao L.-J.,Beijing Institute of Technology | He T.,Jinan Environment Protection Monitoring Center
Wuli Xuebao/Acta Physica Sinica | Year: 2012

A new method of predicting dryness and wetness based on evolutionary modeling (EM) is presented in this paper. Numerical tests indicate that the basic dynamic characteriscs can be captured by EM and the model obtained by EM is not only able to preferablely simulate historical evolution, but also can exactly predict the future evolutionary trend of a time series. For the model obtained by EM with relatively larger prediction errors, the secondary EM can improve the prediction accuracy obviously. © 2012 Chinese Physical Society.

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